Separation functions and optimality conditions in vector optimization
type: Item
instance of: scholarly article
title: Separation functions and optimality conditions in vector optimization
zbMATH Open document ID: 1391.90570
author: Manxue You
author: S. J. Li
published in: Journal of Optimization Theory and Applications
publication date: 2017-12-15T00:00:00Z
review text: The authors consider a constrained vector optimization problem with nonconvex data. They introduce a class of nonconvex separation functions and use them to establish a relationship between efficient/weakly efficient solutions of the problem and associated Lagrangian functions. They derive also Karush-Kuhn-Tucker conditions for the problem when the data are Lipschitz continuous.
reviewed by: Dinh The Luc
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C46
zbMATH DE Number: 6819197
zbMATH Keywords: nonlinear separation
zbMATH Keywords: saddle points
zbMATH Keywords: Karush-Kuhn-Tucker condition
zbMATH Keywords: Lagrangian function
MaRDI profile type: Publication
full work available at URL: s10957-016-1029-4
OpenAlex ID: W2559302081
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DOI: 10.1007/S10957-016-1029-4
description: scientific article; zbMATH DE number 6819197